System and method for optimizing performance of a communication network

ABSTRACT

A communication apparatus and method are provided for predicting effects of changes in at least one radio network parameter on a cellular network which comprises a processor which is adapted to: (a) select a source cell in a cellular network; (b) select from among a first plurality of cells being neighbors of that source cell, a second plurality of neighboring cells and define a reference cluster that includes the source cell and the second plurality of cells; and (c) use the reference cluster to predict the effects of carrying out one or more changes in at least one radio network parameter on at least one network performance indicator of the reference cluster, and based on that prediction, establishing an expected impact of the one or more changes in the at least one radio network parameter on a cellular network performance.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation application (and claims the benefitof priority under 35 U.S.C. § 120) of U.S. patent application Ser. No.15/015,691, filed Feb. 4, 2016, entitled “SYSTEM AMD METHOD FOROPTIMIZING PERFORMANCE OF A COMMUNICATION NETWORK,” Inventors Ziv Nusset al., which is a continuation (and claims the benefit of priorityunder 35 U.S.C. § 120) of U.S. application Ser. No. 14/386,773, filedSep. 19, 2014, entitled “SYSTEM AMD METHOD FOR OPTIMIZING PERFORMANCE OFA COMMUNICATION NETWORK,” Inventors Ziv Nuss et al., now U.S. Pat. No.9,332,458, issued May 3, 2016, and which is a national stage applicationunder 35 U.S.C. § 371 of PCT International Application Serial No.PCT/IL2013/050269, filed on Mar. 20, 2013 and entitled “SYSTEM AMDMETHOD FOR OPTIMIZING PERFORMANCE OF A COMMUNICATION NETWORK”, whichapplication claims the benefit of priority to U.S. ProvisionalApplication Ser. No. 61/615,298, filed on Mar. 25, 2012. The disclosuresof the prior applications are considered part of (and are incorporatedin their entirety by reference in) the disclosure of this application.

TECHNICAL FIELD

The invention relates to a system and a method for managing wirelessnetworks, and in particularly to a system and a method for predictingand optimizing a performance of a cellular communication network.

BACKGROUND

One of the major challenges which any cellular network operator faces isto ensure that the network is operating to its maximum efficiency. As aresult, cellular network optimization is a major feature of many moderncellular networks.

In order to provide the best possible performance to the cellularnetwork subscribers, the network is periodically optimized so that itsresources can be more effectively utilized within the core networkand/or the Radio Access Network (“RAN”).

Typically, network optimization is affected by manually modifyingnetwork parameters in the Radio and Core Networks based on informationthat relates to network performance. Such information is retrievedperiodically and analyzed by the Operations and Support System (OSS) toderive Key Performance Indicators (KPIs) therefrom. The state of the artKPIs include typical system level (e.g. related to user or cellthroughputs) and link level (e.g. various transmission error rates)metrics.

Traditional optimization methods are slow, operate with a high degree ofgranularity, and have a long turnaround time. Optimization of acommunication network using presently available tools basically entailschanging one static parameter setup to another followed by severaliterations of a cumbersome verification stage.

In order to support rapidly changing network needs, it would be highlybeneficial to have a fully integrated automated load balancingapplication with a built in feedback mechanism, thereby freeing theoperators from their tedious roles of manual optimization to softwareapplications and focus on defining network policies, performance goalsand network plans.

Several solutions have been proposed in the art for analyzing awired/wireless communication network to optimize its performance.

US 2005064820 describes continuously collecting data from all elementsconstituting the communication network and analyzing the data to find anelement of which performance and/or efficiency deteriorates.

US 2004085909 discloses scheduling transmissions in a wirelesscommunication system using historical information and usage patterns ofremote users in the system. Usage patterns for users within a system arestored and analyzed to optimize transmissions and resources in thesystem.

US 2010029282 describes collecting various wireless performance metricsby respective network access points as an aggregate measure of thewireless network performance. Aggregated data can be utilized togenerate a performance model for the network and for individual accesspoints. Changes to the data are updated to the model to provide asteady-state characterization of network performance. Wireless resourcesare generated for respective access points in a manner that optimizeswireless performance. Additionally, resource assignments can be updatedat various intervals to re-optimize for existing wireless conditions,whether event driven or based on performance metrics. Accordingly, arobust and dynamic optimization is provided for wireless networkresource provisioning that can accommodate heterogeneous access pointnetworks in a changing topology.

US 20060068712 relates to a method of correlating probed data capturedfrom various interfaces to create a combined picture at a call level.Thus, the method described allows real time distributed analysis andtroubleshooting of the data on the interfaces of N radio networkcontrollers from a single location.

US 20080139197 discloses providing a probe application by a networkserver for downloading by a mobile device. The probe applicationmonitors a level of performance for various use applications provided bythe network for the mobile device, and reports the monitored level ofperformance for at least one of the applications to the network server.The network server collates the performance data from the plurality ofcommunication devices and provides resource allocation instructions tothe mobile in order to optimize a level of performance for the useapplications for the communication device.

Our co-pending application U.S. Ser. No. 13/680,779 filed Nov. 19, 2012describes a computing platform for optimizing operation of a cellularnetwork by: (a) probing for information exchanged between a mobileaccess network and a core network; (b) retrieving statistical KPIsgenerated by a plurality of network elements; (c) predicting a trendcharacterizing future performance of cells; and (d) triggering changesin the operation of the cellular network based on the predicted trend.

However, there is still a need for a solution that provides furtheroptimization capabilities for operating cellular networks, such that cantake into account traffic load effects by using a pre-selected clusterof cells and using parameter settings derived from such considerations,thereby enabling further optimization of the performance of a networkunder near real time conditions.

SUMMARY OF THE DISCLOSURE

The present invention addresses the shortcomings of the presently knownmethods by providing an automated solution for near real timeoptimization of wireless communication networks such as cellularnetworks as well as providing a solution for management of databandwidth allocation.

Unless otherwise specified, all technical and scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which this invention belongs. Methods, devices andsystems similar or equivalent to those described herein can be used inthe implementation or testing of the present invention.

Implementation of the method, apparatus and system of the presentinvention involves performing or completing selected tasks or stepsmanually, automatically, or a combination thereof. Moreover, accordingto actual instrumentation and equipment of preferred embodiments of themethod and system of the present invention, several selected steps couldbe implemented by hardware or by software on any operating system of anyfirmware or a combination thereof. For example, as hardware, selectedsteps of the invention could be implemented as a chip or a circuit. Assoftware, selected steps of the invention could be implemented as aplurality of software instructions being executed by a computer usingany suitable operating system. In any case, selected steps of the methodand system of the invention could be described as being performed by adata processor, such as a computing platform for executing a pluralityof instructions.

The disclosure may be summarized by referring to the appended claims.

It is an object of the present invention to provide a method andapparatus to enable managing traffic load in a cellular network bydiverting traffic between adjacent wireless cells. It is another objectof the present invention to provide a method and apparatus to enablepredicting the impact of offloading mobile users from one cell toanother, upon the performance of the cellular network.

It is still another object of the present invention to provide a methodand apparatus to enable diverting data traffic of mobile stationsbetween wireless cells that belong to a pre-determined cluster of cells.

It is yet another object of the present invention to provide a methodand apparatus to enable diverting traffic of mobile stations betweenwireless cells that belong to a cluster of cells, based on analysis thatwas carried for a different cluster of cells.

Other objects of the present invention will become apparent from thefollowing description.

According to a first aspect, there is provided a communication apparatusoperative to predict effects of changes in at least one radio networkparameter on a cellular network which comprises one or more processorsadapted to:

(a) select a first cell (a.k.a. a source/main cell) in a cellularnetwork;(b) select from among a first plurality of cells being neighbors of saidfirst cell, a second plurality of specific neighboring cells (preferablybeing communication-dependent on the first cell) and defining areference cluster that includes the first cell and the second pluralityof cells; and(c) use the reference cluster to predict effects of carrying out one ormore changes in at least one radio network parameter on at least onenetwork performance indicator of said cluster, and based on thatprediction, establish an expected impact of the one or more changes inthe at least one radio network parameter on a cellular networkperformance.

It should be noted however, that the number of cells included in thefirst plurality of cells may be equal to or greater than the number ofcells included in the second plurality of cells.

In accordance with another embodiment, the cells which are selected fromamong the first plurality of cells to belong to the second plurality ofcells, if:

(i) a number of handovers carried out from the first cell to each of theselected second plurality of cells within a pre-defined period of time,divided by a total number of handovers carried out from the first cellto all of its neighboring cells belonging to the first plurality ofcells within that pre-defined period of time exceeds a pre-definedthreshold; and/or

(ii) a geographical distance extending between said first cell and eachof the selected second plurality of cells is equal to or less than apredetermined value.

By yet another embodiment, the at least one radio network parameterbeing changed is offloading of communication traffic from the first cellto at least one cell from among the second plurality of cells.

According to still another embodiment, the at least one radio networkparameter is a member of the group that consists of: antenna tilt, pilotpower usage and/or handover hysteresis offset between the first cell andthe second plurality of cells.

In accordance with another embodiment, the first cell is characterizedby having radio resource utilization which exceeds a predeterminedthreshold.

By still another embodiment, the one or more processor are adapted torepeat (c) until the expected impact on the cellular networkperformance, of the one or more changes in the at least one radionetwork parameter, is maximized.

According to another embodiment, the at least one radio networkparameter change leading to maximization of impact on the cellularnetwork performance, is applied for optimizing cellular networkperformance associated with a second cluster.

By yet another embodiment, the communication apparatus is adapted foruse in a process of balancing a traffic load of the cellular network,wherein the one or more processors are further adapted to:

(I) use the reference cluster to determine an effect of carrying out oneor more changes in at least one radio network parameter on at least onenetwork performance indicator of the reference cluster, and based onthat determination, derive traffic load optimization rules for thecellular network; and

(II) obtain at least one network performance indicator which isassociated with the cellular network and optimize load performance ofthe cellular network according to the at least one network performanceindicator and the load optimization rules.

According to another aspect, a method is provided for predicting effectsof changes in at least one radio network parameter on a cellularnetwork, wherein the method comprises the steps of:

(a) selecting a first (source) cell in a cellular network;(b) selecting from among a first plurality of cells being neighbors ofthe first cell, a second plurality of specific neighboring cells beingcommunication-dependent on said first cell and establishing a referencecluster that includes the first cell and the second plurality of cells;and(c) using the reference cluster established to predict effects ofcarrying out one or more changes in at least one radio network parameteron at least one network performance indicator of the reference cluster,and based on that prediction, establishing an expected impact of the oneor more changes in the at least one radio network parameter on acellular network performance.

According to another embodiment of this aspect, the method provided isused in a process of balancing traffic loads in the cellular network,and wherein the method further comprising the steps of:

(I) using the reference cluster to determine an effect of carrying outone or more changes in at least one radio network parameter on at leastone network performance indicator of the reference cluster, and based onthat determination, derive traffic load optimization rules for thecellular network; and

(II) obtaining at least one network performance indicator which isassociated with the cellular network and optimize load performance ofthe cellular network according to the at least one network performanceindicator and the load optimization rules.

By yet another embodiment, the method is used in managing data radioresources of a cellular network, wherein the method further comprising:

(a) retrieving information that relates to:

(i) radio resource load conditions of a cell; and

(ii) radio conditions for each user of that cell;

(b) identifying data-overloaded cells and correlating their associatedinformation with that retrieved in (a); and(c) ranking users of these cells according to their impact on radio loadof the cell.According to still another embodiment, the method further comprising:(d) limiting data provisioning to specific users of the cell based onstep (c) and subscriber information associated with these specificusers.

It should be noted however, that even in case where no change inperformed in any of the cells of the reference cluster, still, theinvention should be understood to cover affecting changes at the sourcecell and impact the usage and loading pattern of cells in the area, inorder to balance the load between the cells located in that area. Thismay in fact be regarded as being intra carrier spatial load balancing.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, reference isnow made to the following detailed description taken in conjunction withthe accompanying drawings wherein:

FIG. 1 illustrates a system that comprises a reference cells' clusterincluding a source cell and its cluster-specific neighboring cells andthe radio network and optimization network (cSON) servers;

FIG. 2 is a flowchart outlining steps in the process of generating areference cluster (A) and the use of specific rules in load balancing oftraffic applied to a specific cluster (B);

FIGS. 3-4 illustrate typical prior art architecture of PCRF/PCEF in anIMS framework;

FIG. 5 illustrates one configuration according to an embodiment of thepresent invention which enables communication between the PCRF node andthe cSON server of the present invention;

FIG. 6 illustrates results of user's radio quality sampled over 3minutes and their distribution per cells in the radio network.

FIG. 7-10 illustrate the effect of load balancing as practiced using thesolution of the present invention on radio resource load and relevantKPI trends.

FIG. 11 illustrates daily hardware load patterns for a specific sourcecell of a cluster showing the effect of activating the load balancing(LB) algorithm in the last four days of monitoring (arrows).

DETAILED DESCRIPTION

According to one embodiment, the present invention relates to a systemwhich utilizes predefined rules for near real time optimization of acellular network performance. Specifically, this embodiment of thepresent invention can be used to automate the task of networkperformance optimization and provide in near real time networkperformance gains in cells that are characterized by suboptimalperformance as indicated by relevant KPIs.

The principles and implementation of the present invention may be betterunderstood with reference to the drawings and accompanying descriptions.

In this disclosure, the term “comprising” is intended to have anopen-ended meaning so that when a first element is stated as comprisinga second element, the first element may also include one or more otherelements that are not necessarily identified or described herein, orrecited in the claims.

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a betterunderstanding of the present invention by way of examples. It should beapparent, however, that the present invention may be practiced withoutthese specific details.

Operators of cellular networks are facing nowadays an avalanche ofdemand, driven by the mobile data crunch-fast penetration of smartphonesand mobile broadband. In order for them to support this increase intraffic, it requires to introduce proportional increase in resources,causing a linear increase in under-utilized resources.

Cellular networks have dynamic RF traffic patterns that changethroughout the week, and over the course of a day. Such dynamic patternsresult from changes in voice and data communication loads, geographicalchanges in the position of the user equipment (UE) and the like.Unexpected load imbalances due to massive gatherings, cell malfunctionor introduction of new cells in an area all effect the loaddistribution, and have to be dealt with as soon as they occur.

In static networks with no time-sharing of resources, costly resources(e.g. cells) which can be used to support peak traffic are obviouslyoften under-utilized. Although dimensioning rules dictate adding newcells or resources when peak capacity approaches saturation, the load inunbalanced networks is uneven, and hence dimensioning rules are notapplicable.

On the other hand, leaving the cellular network in unbalanced statuswithout expansion, will limit the data/voice capacity that can be madeavailable to subscribers at peak times, and consequently lowersubscribers' satisfaction leading to a possible loss of revenue.

Existing optimization solutions are affected through large time cycles.It might take days or even weeks to optimize a network. By their verynature, such solutions are suitable for long term or predicted loadissues and not for dealing with immediate load imbalances.

Using decision-supporting software to perform the optimizationcalculations such as required expansions, RF parameter changes, and thepredicted impact on performance, gives the radio engineers moreoptimization choices than using manual input. However, these toolsprovide reports—not actions, and are prone to error due to the highdegree of sensitivity to initial conditions. The radio engineers arestill left with the tasks of verifying the resulting recommendations,updating the cells configurations, and checking the results. This is anopen-loop solution, where the entire end-to-end process still requiresmanual stages in order to complete it.

Some equipment vendors offer solutions of inter-frequency loadbalancing. These solutions can balance loads between carriers—generallyco-sectors on the same pole.

These solutions, while efficient in resolving localized load imbalancecases, do not provide a solution for a load in a specific group ofcells, require installation of infrastructure of multiple carriers, donot solve problems near the cells' edges and do not optimize theutilization of a single carrier layer.

For local areas with consistent capacity problems, operators can electto offload the data portion to another layer such as Wi-Fi, LTE or smallcells. However, in order to extract value from Wi-Fi offload mobileoperators will require carrier-grade Wi-Fi networks that are tightlyintegrated into the operator's network and back office environments. Inany case, such a solution can only add capacity to a fixed location, butdoes not provide a solution to congestion situations that change overtime and place.

The above described solutions are not designed to be compatible with thedegree and extent of usage variations, typically encountered in presentday cellular networks and have practical limitations such as requiredhandset support or multi vendor functional complexities.

To traverse these limitations of prior art optimization approaches, thepresent invention devised an automatic optimization solution (referredto herein as a self optimized network—SON) which can respond to demandpatterns as they form and change.

The present invention relates among others to a method (e.g. carried outas an algorithm, referred to hereinafter as LB algorithm) whichcomprises the following steps:

(i) selecting a specific cell (typically a resource loaded cell) being afirst cell and several of its neighbors (from among all its neighbors)and forming a cluster of cells where that specific cell is the main cellof the cluster;

(ii) changing radio network parameters of the cluster;

(iii) monitoring the effect of such changes on KPIs of the main (source)cell or cluster;

(iv) repeating steps (ii) and (iii) until resource loading of the maincell (or cluster) is decreased.

The results gained by following such a process can be applied tooptimize performance of the same cluster following the cluster formationand at different time points (e.g. Example 3), as well as to optimizeperformance of an identical or similar cluster (or a main cell thereof).

As further described in the Examples section which follows, use of theSON system of the present invention can lead to dramatic improvement inoverall network resources' utilization, reduction in the number ofloaded cells and noticeable improvement in the end user QoE (Quality ofExperience).

Thus, according to one embodiment of the present invention there isprovided a system for monitoring the effect of changes in radio networkparameter (s) on manageable resources of a communication network such asa cellular network (which can include WI-FI, 802.lla/b/g/n, GSM, UMTS,EVDO, LTE, LTE-A, WiMax 802.16e and others).

As used herein the terms “manageable resources” or “resources” refer toutilization (or over-utilization) of various network resources,including available transmission power of the base station poweramplifier, baseband processing capacity available in the basebandhardware cards installed in the base station, available codes in theOVSF code tree in UMTS/CDMA technology, or available PRBs (PhysicalResource Blocks) in LTE technology, or the accumulated uplink noisecorrelated to the carried traffic by the cell. Additional resourceswhich can be affected and managed by the present invention includetransmission (or backhaul) link to the base stations, RNC (Radio NetworkController) resources such as MP (Main Processor) load etc.

As used herein the terms “Radio Network Parameters” and “radioparameters” interchangeably refer to various parameters which can havean effect on the consumption and performance efficiency of the cellularnetwork resources. Such parameters include both software and/or hardwareradio parameters, e.g. CPICH power settings, vertical tilt anglesettings, handover threshold settings, handover offsets betweendifferent cells, and the like.

Such radio parameters changes affect the network performance e.g. itsability to support voice and data communication with UEs, its powerconsumption, and all other network resources described above such aspower usage, code usage, etc.

The system according to an embodiment of the present invention includesa computing platform (e.g. hardware running a dedicated softwareapplication, for example a standard HP Proliant G7 server running thesoftware application) which is in communication with the OSS (Operationand Support System) of the network.

The computing platform is in communication with individual cells of thecellular network by direct connection to the network elements or througha mediation layer such as an OSS server connection and is capable ofselecting a specific cell of a cellular network to be the first cell(a.k.a. source cell, main cell) according to one or more of thefollowing criteria:

(i) Power usage of the cell exceeds a predefined threshold;

(ii) Code tree usage of the cell exceeds a predefined threshold;

(iii) Baseband hardware resources of the cell exceed a pre-definedthreshold;

(iv) Transmission resources consumed by the cell exceed a pre-definedthreshold;

(v) Number of CS and/or PS links supported by the cell exceeds apre-defined threshold;

(vi) Number of HS (High Speed) links served by the cell exceeds apre-defined threshold;

(vii) Number/Percent of rejected PS/CS/HS establishment attempts whichare rejected by the Admission Control mechanism implemented by the RNCor by the cell itself, exceeds a pre-defined threshold.

(viii) Data Traffic payload to Voice traffic Erlang carried by the cellexceeds a pre-defined threshold.

Once such a source cell is selected, the computing platform selects froma list of its neighbors (i.e. a first plurality of cells), a number ofspecific neighbor cells (being a second plurality of cells selected fromthe first plurality of cells) that are dependent on that source cell inas far as communication capabilities, i.e. neighbor cells that are RFrelated to the source cell. The selected neighbor cells are preferablycells that are highly coupled from RF perspective to the source cell. Inother words, they have substantial overlapping with the source cell.This may be expressed by the relative number of handover attemptsbetween any destination cell to the source cell.

Selecting such specific neighbor cells may be done according to one ormore of the following criteria:

(i) Cells that are defined as intra frequency neighbor cells to thesource cell either in the management system or by the cell itself (forexample in the case of LTE ANR mechanism); and/or

(ii) A weighting function is implemented for the neighbor cells andcertain neighbors are selected according to a weighting rankingalgorithm. A possible weighting function may be the relative number ofHO attempts (or successes) between any relation to the overall number ofHO attempts (or successes) measured for the cell. Another possibleweighting function may be for example the relative number of measurementreports from UEs under the domain of the source cell that report thespecific neighbor cell as present in the Active Set (for CDMAtechnology) or as exceeding a certain signal strength threshold (for anytype of technology). Once the source cell and the specific neighbors areselected, the computing platform establishes a monitored cluster (alsoreferred to herein as reference cluster) which comprises one or more ofthe following combinations:

(i) the source cell and the specific neighbor cells;

(ii) the source cell and all of the cells being a first degree neighborsof the source cell; and

(iii) the source cell and a combination of cells that comprises firstand second degree neighbors, or Nth degree neighbor of the cellaccording to their calculated weight, such that the final weightcalculated for any specific neighbor is above a predetermined threshold.

The cluster described above is monitored to identify effects of changesin the network parameter(s) on the operating performance within thecells belonging to the cluster being monitored or with the main cell ofthat cluster itself. The source cell/cluster performance can be measuredeither by retrieving values of KPIs collected from the OSS system, or byany other near real time means such as probe based calculated KPIs, toidentify the impact of changes induced in the network parameter (s) onthe cellular network. The relationship between the changes of theparameter (s) and KPIs may be used to establish a set of optimizationrules which are applied to the cluster in a continuous fashion until aperformance thereof or of its source cell is optimized.

Such optimization rules may include, but are not limited to, thefollowing:

(i) change of CPICH (Common Pilot Channel) by −Δ₁ dB for the sourcecell, and +Δ₂ dB for the selected neighbor cells;

(ii) change HO offset for the source cell by −Δ₁ dB, and +Δ₂ dB for theselected neighbor cells; and/or

(iii) change tilt by −Δ₁° for the source cell and by −Δ₂° for theselected neighbor cells.

Some or all of the above optimization steps can be repeated in apredefined order, until the cause of source cell over-utilization(loading), e.g. power load, falls below a threshold, or untilperformance degradation occurs. Once performance degradation isdetected, the system reverts to the last change prior to thatdegradation. This is implemented using a feedback function whichconstantly monitors the reference cluster performances in terms of forexample drop call rate, number of voice and data calls, HS payload andthroughput. In addition, under-layer cells to the reference clustercells may also be monitored, such as cells associated with another layer(e.g. GSM or another UMTS carrier) to confirm that no change in the KPIpattern has occurred in those under-layer cells.

A system for modeling and optimizing a communication network, which isreferred to herein as system 10 is illustrated in FIG. 1.

System 10 includes a cluster of cells 12 which includes a source cell 14and several (typically, between 4-6) neighbors' cells 16. The neighborscan have the same frequency as the source cell (referred to herein as“Intra cluster”) or a different frequency (referred to herein as “Intercluster”), or there may be cases where different technologies isimplemented in one or more of the neighbor cells from that of the sourcecell (referred to herein as “iRAT cluster”).

Each of cells 14 and 16 is in communication with the radio networkcontroller 18 (RNC) or another equivalent mobility control entity, orOSS, or cSON server, in other technologies, which is in turn connectedto the core network servers 20 and the OSS servers 22 which include anOSS database 24.

For a cSON server, all the coordination is carried out internally, sincethe cSON server is operative to control directly control all the cellsin the network, and can have a centralized view of the KPI effect to anychange in performance resulting from radio parameters' changes. In a noncentralized configuration of SON, a communicating and coordinatingfunction between SON functions which control subset of the network maybe used, to allow site specific load balancing activities, andperformance monitoring.

A flow chart illustrating a cluster setup is shown in FIG. 2 (steps1-5). The present system (referred to as system 50—further describedherein below) is synchronized every several minutes (e.g. 3-10 minutes)with OSS 24 in order to obtain KPIs of the network (step 1). In step 2,the system monitors all cells of a network for load metrics (monitoringis implemented by applying specific load balancing (LB) algorithms whichare executed by cSON server 52), thereafter congested cell (or cells)are identified by cSON 52 (step 3) based on load metrics. Such acongested cell will be determined to be the source cell. If no sourcecell is identified, system 50 reverts to step 1 (step 4). The LBalgorithm of the present invention then identifies the relevantoffloading neighbor cells to the source cell and determines theirloading status (step 5); the source cell and its offloading neighborsand then defined as the cluster to be monitored.

As mentioned hereinabove, such a cluster can be used for optimizing theperformance of the source cell and/or of the cluster and/or a sourcecell belonging to an identical or similar cluster.

Thus, according to another aspect of the present invention there isprovided a system for optimizing network performance in a cellularnetwork.

System 50 illustrated in FIG. 1 includes an optimization server (cSON)52 and database 54 communicating with OSS servers 22.

Load balancing optimization is based on constant KPI monitoring.Therefore, system 50 continuously extracts KPI values from the network(from the performance management database of OSS 20) and provides theseKPI values to the SON application for analysis. A load balancingapplication executed by server 52 checks a list of load and admissioncontrol statistics (which are stored at cSON DB 54), and compares themto thresholds configured by cSON users (e.g. optimization engineersrunning the cSON system on a routine basis). When any of thesethresholds are exceeded in any cell of the network, this cell isdetermined as a “source cell”, cells (selected neighbor cells of thesource cell) are added to a work list, and the application initiatesre-balancing of network resources' consumption of users by means of RFshaping of the loaded cell and the surrounding cells. By fine tuning thesettings of these thresholds, the operator can deal with load conditionseven before the load can actually be felt by subscribers. Theperformance of the re-balancing process highly depends on the accuracyof the neighbor relation lists and on the configured time constants ofthe application.

System 50 provides a near-real time response (typically minutes) to therapidly changing and unpredictable load demands imposed on the network.The Load Balancing application of server 52 modifies the RF footprint ofthe loaded and surrounding cells to fit the current usage demand andmatch the subscriber distribution to the available resources. Using RFshaping increases the efficiency of the network, and increases theutilization of existing resources.

The Load balancing application of server 52 determines the RF parametersfor the loaded cell and its neighboring cells based on the KPI and PMdata collected from the OSS.

In order to ensure that RF shaping may indeed be carried out withoutdamaging the quality of service for cell edge users, namely—loosecoverage, the solution provided by the present invention enablesutilizing a metric of cells' overlap which has to exceed a thresholdbefore a load balancing procedure may be initiated. In a UMTS (UniversalMobile Telecommunications System) for example, this metric is calculatedas the Soft Handover Factor of the source cell and is used to indicatethe influence of soft handover exerted on NodeB CE and to evaluate thesubscriber resource utilization. If this Soft Handover factor is highenough, e.g. >1.6, then the load balancing procedure may be executedwhen this cell becomes congested.

Optimization of a cluster is described by steps 6-10 of the flow chartof FIG. 2. Using the cluster generated in steps 1-5, the LB algorithmthen changes the radio parameters of the cluster to implement trafficoffloading from the source cell to its less loaded neighbor (s) (step6). Source cell and/or cluster performance is then monitored (step 7)and a determination is made (based on a performance threshold) whetherto implement further changes to enable further offloading, to remain atlast state, or to revert to the previous state (by canceling theparameter change). Steps 6-7 are then repeated until a performancethreshold is achieved as determined by the rate of successful callinitiations or any other applicable measure (step 8). If degradation inthe quality of service occurs, system 50 reverts to the initial radioparameter settings (step 9). If the system cannot obtain KPIs for apredetermined time period, the status is reset and the cluster isreverted to its initial radio parameter settings (step 10).

The above description relates to optimization procedure of a generatedcluster, by conducting several iterative steps of parameters' changesand KPI monitoring. However, it should be noted that the resultsobtained from optimizing such a cluster (termed herein as a referencecluster) can be applied to optimizing network performance of otherloaded cells that can form a cluster similar to or identical to thecurrently optimized cluster.

For example, clusters in which the user radio map and radio resourceutilization of the cells are substantially identical to that of thereference cluster can be generated (as described in steps 1-5) and thenbe optimized by simply applying the radio parameter changes that lead tosuccessful optimization of the reference cluster. This negates the needfor the time consuming iterative process described in steps 6-8 of FIG.2. The user radio map and radio resource utilization of substantiallyidentical clusters can be determined by comparing the overalldistribution of quality metric such as Ec/Io and signal strengthindicator such as RSCP.

In another example, in cases where the load balancing procedure istriggered repeatedly every day at same cell (FIG. 11), the parameterchange information can be used to drive coverage and capacityoptimization (CCO) in order to plan and implement (automatically) aconstant change to the RF footprint of the source cell and of some ofits neighbor cells by using a single step. In such cases, predeterminedradio parameter changes can be applied to specific clusters at specifictimes of the day, or days of the week, without having to go through theiterative optimization steps as depicted in FIG. 2.

According to an embodiment of the present invention, the system providedcan identify different cluster types and store information that relatesto such clusters along with information on optimization and variousradio parameters (e.g. user radio map and radio resource utilization) ata database.

The clusters can then be categorized according to one or more of thefollowing:

(i) voice and data traffic being conveyed within the cluster;

(ii) traffic distribution between the cells that belong to the cluster;

(iii) radio conditions of each cell comprised the cluster and of thecluster in overall;

(iv) handover statistics and soft handover (SHO) factor between thecells that belong to the cluster;

(v) radio resources configuration of the cells included within thecluster;

(vi) radio hardware configuration of the cells included within thecluster; and

(vii) radio software parameter settings of the cells included within thecluster.

Once a specific cluster category is generated, it can be used later one.g. in other SON applications.

The database may also be used to store details associated with the SONactivity (for example, LB procedures activated per cell in the cluster)for each cluster configuration as well as performance metrics (KPIs) foreach cluster. The system can also generate and store KPI performancetrends for each cluster type, and create a predictive function that willenable predicting the KPI behavior of any cluster based on similarcluster types stored in the DB.

It will be appreciated by those skilled in the art that although loadbalancing according to the present invention can be applied tosubstantially enhance the performance of the network and thus to betteraccommodate the subscribers' demands, mobile operators today are facingan avalanche of demand which is driven in part by heavy mobile datademand. Thus, to further support this increase in data traffic, thesolution provided by the present invention also offers a novel approachfor enhancing policy control and resource management in cellularnetworks.

At present, operators utilize a node or nodes for policy and chargingrule function (referred to herein as PCRF) and DPI techniques torestrict and manage data sessions regardless of radio resourceconsumption. In presently deployed 3G/4G mobile networks, data trafficis streamed from the user through the radio network (UTRAN/E-UTRAN) andpacket core (PS-Core/EPC) to the Internet.

An IP multimedia subsystem (IMS) is a framework defined by 3GPP Standardto provide Internet and data services over cellular networks. Part ofthe IMS framework is the PCRF/PCEF which relates to two nodes configuredto provide a platform for policy and charging rules function (PCRF) andpolicy and charging enforcement function (PCEF). The PCRF nodedetermines, in real time and according to various considerations, a setof rules governing the way that data user traffic is handled. Suchconsiderations normally include subscriber's subscription data (fromHSS), QoS approved level, network load, operator service policy etc.

The main purpose of the PCRF is to enable management of the core networkresources effectively in order to provide the best suited Quality ofService to users of data services.

A typical architecture of PCRF/PCEF in IMS framework is illustrated inFIGS. 3 and 4. The PCEF node is designated to perform DPI (Deep PacketInspection) into user traffic and enforce the rules created by the PCRFin real time. The PCRF node interacts with various network elements anduses several types of information, such as user's subscription records,allowed QoS levels for users and prioritization of services. The PCRFutilizes this information to create rules for enforcing bandwidthconsumption limits per user (per PDP context) which are compatible withthe user's contractual terms and with the operator's services'priorities, e.g. VoIP is prioritized over Streaming etc.

For example, service providers can use PCRF to charge subscribers basedon their volume of usage of high-bandwidth applications, charge extrafor QoS guarantees, restrict applications' usage while the user isroaming, or lower the bandwidth of wireless subscribers usingheavy-bandwidth apps during peak usage times. PCRF can also be used torestrict user data traffic selectively to handle load situations innetworks.

Currently the PCRF and PCEF nodes are not “aware” of cases in whichoverloaded cells are serving users with low bandwidth needs in lowcoverage areas. Such users may overload the radio resources of a cellwhich is designed to limit high bandwidth users that are near the cell.

The present invention provides a solution to such cases by managingspecific data links that consume radio resources in order to reducetraffic loads from cells having high radio-resource utilization. This isachieved by providing the core bandwidth management systems (PCRF andPCEF) with information regarding users in specific radio-overloadedcells (e.g. with very limited remaining-radio resources), allowing suchsystems to apply specific policies to users who consume radio-resourcesfor reducing the radio-resource loads on such cells.

FIG. 5 illustrates one approach for enabling communication between thePCRF node and the cSON server of the present invention thus making thePCRF ‘aware’ of users in specific cells that have high radio-loadinformation in the UTRAN. The information communicated from the cSONserver to the PCRF node preferably includes cell level details ofradio-resource loads for each and every cell, as well as radio resourceconsumption data for users present in each cell at any given moment. ThePCRF node can use this information to selectively restrict specificusers consuming high radio resources, to identify users consuming lowerpriority services, or users having a lower priority SLA (Service LevelAgreement) in order to make sure that users of high priority SLAs areprovided with the best data service.

To enable such optimization of data bandwidth provisioning, the cSONserver may periodically provide the PCRF node (under near real timeconditions) with UTRAN and UEs load information such as:

(i) list of all cells and their radio resource load conditions (power,channel elements, codes) and backhaul; and/or

(ii) list of all users identified by IMSI (unique UE identifier).

-   -   For each user the information may include:    -   (1) To which cell is the user connected in the sampled time        period (in cases where a user was identified in different cells        during the sample time period, the identification of the latest        cell is provided). Optionally, a “mobility filter” may be        applied, by which, if during the sample time the user is active        in a number of cells and this number is higher than a threshold,        then user will be designated as a “high mobility user”.    -   (2) The measured radio conditions for the user in the last        serving-cell (average during relevant sample time).

(iii) cSON Load balancing activity status on all the cells (e.g. is itactive at the cell, what action was performed, etc.).

The PCRF node then assess which cells are overloaded from data backhauland data usage perspective (may be based also on information retrievedfrom other sources) and correlates this information with informationprovided by the cSON (as discussed above). For each loaded cell havingalso radio resource loaded, the PCRF node uses the provided subscriberpotential load information in conjunction with other subscriber relatedinformation, to determine to which of the subscribers active in acertain cell, their activity will be restricted in that cell.

By applying selective restriction on a “per user” basis to the RR loadedcells, the actions taken by the PCRF node are more accurate and willensure efficient usage of the UTRAN radio resources in compliance withthe operator charging, service priorities and user SLAs according totheir contracts.

FIG. 6 illustrates user radio quality sampled over 3 minutes and theirdistribution per cells in the radio network. The average quality (AvgQuality) indicates the users' potential to consume high radio resourceseven while using low bit rate applications; the PCRF node willprioritize restriction of the activities of users with low Avg quality.

Additional objects, advantages, and novel features of the presentinvention will become apparent to one ordinarily skilled in the art uponexamination of the following examples, which are not intended to belimiting.

EXAMPLES

Reference is now made to the following examples, which, together withthe above description illustrate the invention in a non limitingfashion.

Example 1 Cluster Setup

In order to create a reference cluster for a congested cell, the systemconsiders all the neighboring cells of that cell, e.g. cells that aredefined by the OSS as being neighbors of this cell. Then, for each oneof the neighbors, the system calculates a weighing function. Theweighting function represents the intensity of the RF influence on usersconnected to the source cell, from a respective neighbor cell. Forexample, if for source cell A the weight of neighbor cell B is 10%, itmeans that in a given time interval (which is typically the weighingaveraging window) 10% of the users which were connected to A, wouldexperience B as the strongest neighbor or one of the top N strongestneighbors [where N depends on the active set size parameter which isdefined in the RNC]. According to neighbor weights, the system selectsthe top weighted M (M=5 typically) neighboring cells, and relates tothose cells as being the cluster neighbors for traffic offloading andfor performance monitoring.

Example 2 Load Balancing

In this example, two clusters (specified in tables 1 and 2) weregenerated, monitored and optimized using the LB algorithm in accordancewith an embodiment of the present invention.

Main Cell 103417060299 (Table 1) had a power load which exceeded thedefined power load threshold. The system identified the top 5 neighborsof this main (source) cell according to their weight (Table 1).

Additional filtering of the top 5 neighbors in the reference cluster wasperformed by selecting neighbors that can be used for offloading trafficfrom the source cell. Only neighboring cells which were not congestedwere selected for offloading traffic as shown in Table 1. The systemthen determined the action needed for each neighbor. The action in thiscase was to change the CPICH (pilot channel) transmission power from itscurrent value by some offset in dB.

Once all radio parameter changes were applied by the system to neighborcells, performance is monitored and a return (to initial settings) causeis logged. The same procedure was applied to the cluster of Table 2.

Cluster of Table 1 relates to return to initial settings followingnormal LB timeout (4 h). In the cluster of Table 2, there is an abnormalreturn as the system had identified missing KPI samples which led to itsinability to monitor the performance effect of LB.

TABLE 1 Neighbor Neighbor Selected Main Cell id Load Trigger cell idweight for LB Cause/Action Revert Cause 103417060299 ‘load_power’ Maincell - 103417060299 Yes level 1 Feedback 103417062599 5.3%; No CPICH 298of neighbor ended is already at is maximum. After 4 hours Can't increaseit 103417063198 14.9%; No Discarded neighbors - ‘is_active’ 10341706169736.4%; Yes Changing CPICH of neighbor from 240 to 245 103417061609 7.9%;No CPICH 305 of neighbor is already at its maximum. Can't increase it103417060298 10.40% No CPICH 305 of neighbor is already at its maximum.Can't increase it

TABLE 2 Neighbor Neighbor Selected Main Cell id Load Trigger cell idweight for LB Cause/Action Revert Cause 1034490005187 ‘load_power’ Maincell - 103449005187 Yes level 1 + level 2 “Cell had too 1034490330576.50% No Discarded neighbor - many missing ‘load_power’ KPIs and will be103449032028 5.10% Yes Changing CPICH of reverted” - After neighbor from300 to 305 50 Min. 103449002268 5.30% Yes Changing CPICH of neighborfrom 310 to 312 103449031459 6.60% No CPICH 310 of neighbor is alreadyat its maximum. Can't increase it 103449031398 11.80% Yes Changing CPICHof neighbor from 281 to 286 103449032029 16.80% No Discarded neighbor -‘load_power’ 103449005188 16.70% No CPICH 316 of neighbor is already atis maximum. Can't increase it 103449030707 5.80% No Discarded neighbor -“load_power’, ‘is_active’

FIGS. 7-10 illustrate the effect of load balancing using the system ofthe present invention solution on KPIs and load metrics (as derived fromOSS) of the cells described in Tables 1 and 2 above. Numbers on theright of the Figure note the cell ID which corresponds to the cell ID inthe Tables.

FIG. 7 illustrates the power load of the main cell of the cluster andits neighbors (presented in Table 1). As shown in this Figure, applyingthe LB algorithm in accordance with the solution provided by the presentinvention starting at 1:30:00, resulted in a dramatic decrease oftraffic load in the main cell of main (source) cell ‘299, withoutoverloading the neighbor cells.

FIG. 8 illustrates the power load of the main cell of the cluster andits neighbors (Table 2 without discarded neighbors) showing thatapplication of the LB algorithm of the present invention solutionresulted in a load drop. However, due to the fact that KPIs could not beretrieved for the main cell of this cluster following time point11:15:00, the system stopped the LB application and reverted to itsinitial settings.

FIG. 9 illustrates power load vs. RRC_Succ (Accessibility KPIs—indicatesrate of successful call initiation) for main cell 187 (Table 2). Asillustrated in this Figure, application of the LB algorithm of thepresent invention resulted in power load decrease and a significantincrease in RRC_Succ. As was noted for FIG. 8, KPIs for this source cellcould not be retrieved beyond time point 11:15:00 and as such the systemreverted to its initial settings.

FIG. 10 illustrates the same monitoring for main cell 299 of Table 1. Asillustrated in this Figure, activation of LB algorithm (at time point1:30:00) resulted in a decrease in power load and increase in RRC_Succ.

Example 3 Load Balancing-Reduced Hardware Load

FIG. 11 illustrates the effect of applying the LB algorithm on the samecluster at specific time points during the day, the last 4 peaks in thegraph represent days in which the present load balancing algorithm wasutilized to reduce load of a main cell. In this case LB action can bepermanent, subject to the rules and conditions of the CCO (Coverage andCapacity Optimization) platform. This is due to the fact that LB changesare consistent at a specific time point each day (busy hour) and assuch, the same radio parameters can be applied at these time points.

In the description and claims of the present application, each of theverbs, “comprise” “include” and “have”, and conjugates thereof, are usedto indicate that the object or objects of the verb are not necessarily acomplete listing of members, components, elements or parts of thesubject or subjects of the verb.

The present invention has been described using detailed descriptions ofembodiments thereof that are provided by way of example and are notintended to limit the scope of the invention in any way. The describedembodiments comprise different features, not all of which are requiredin all embodiments of the invention. Some embodiments of the presentinvention utilize only some of the features or possible combinations ofthe features. Variations of embodiments of the present invention thatare described and embodiments of the present invention comprisingdifferent combinations of features noted in the described embodimentswill occur to persons of the art. The scope of the invention is limitedonly by the following claims.

What is claimed is:
 1. A method comprising: changing an initial value ofat least one radio network parameter for a reference cluster of cells,wherein the reference cluster of cells includes a first cell and aplurality of cells that neighbor the first cell and the plurality ofcells comprise at least one of intra cluster, inter cluster, or InterRadio Access Technologies (iRAT) cluster; monitoring resource loadingand at least one performance indicator of the reference cluster ofcells; and repeating the changing and the monitoring until one of:resource loading of the reference cluster of cells is decreased below aresource loading threshold, or the at least one performance indicatorindicates degraded performance for the reference cluster of cells. 2.The method of claim 1, wherein the at least one radio network parameteris offloading of communication traffic from the first cell to at leastone cell from among the plurality of cells that neighbor the first cell.3. The method of claim 1, further comprising: selecting the first cellfor the reference cluster of cells in a cellular network, the first cellis characterized by having a radio resource utilization that exceeds apredetermined threshold.
 4. The method of claim 1, further comprising:selecting each of the plurality of cells that neighbor the first cellfor the reference cluster of cells based on at least one of: each of theplurality of cells being defined as intra frequency neighbor cells ofthe first cell, forming the intra cluster, or each of the plurality ofcells having a signal strength as measured by user equipment associatedwith the first cell that exceeds one or more signal strength thresholds.5. The method of claim 4, further comprising: ranking each of theplurality of cells according to the signal strength to determinedifferent degrees of the plurality of cells that neighbor the firstcell.
 6. The method of claim 1, further comprising: based on a failureto obtain from the monitoring the at least one performance indicator ofthe reference cluster of cells for a predetermined time period, settingthe at least one radio network parameter to the initial value.
 7. Themethod of claim 1, wherein the at least one radio network parameterincludes hardware and software radio parameters, and wherein the initialvalue of the at least one radio network parameter is decreased for thefirst cell and increased for at least one of the plurality of cells thatneighbor the first cell.
 8. The method of claim 1, wherein the resourceloading includes a rate of successful call initiation for the firstcell.
 9. A method comprising: changing an initial value of at least oneradio network parameter for a reference cluster of cells, wherein thereference cluster of cells includes a first cell and a plurality ofcells that neighbor the first cell, and wherein the at least one radionetwork parameter is one or more of: an antenna tilt, a common pilotchannel (CPICH) power, and a handover hysteresis offset; monitoringresource loading and at least one performance indicator of the referencecluster of cells; and repeating the changing and the monitoring untilone of: resource loading of the reference cluster of cells is decreasedbelow a resource loading threshold, or the at least one performanceindicator indicates degraded performance for the reference cluster ofcells.
 10. The method of claim 9, further comprising: selecting thefirst cell for the reference cluster of cells in a cellular network, thefirst cell is characterized by having a utilization of a radio resourcethat exceeds a predetermined threshold.
 11. The method of claim 10,wherein the radio resource includes at least one of: a power usage ofthe first cell, code tree usage of the first cell, baseband hardwareresources of the first cell, transmission resources of the first cell, anumber of high speed links serviced by the first cell, a percentage ofrejected call establishment attempts, or data and voice traffic carriedby the first cell.
 12. The method of claim 9, further comprising:selecting each of the plurality of cells that neighbor the first cellfor the reference cluster of cells based on at least one of: each of theplurality of cells being defined as intra frequency neighbor cells ofthe first cell, or each of the plurality of cells having a signalstrength as measured by user equipment associated with the first cellthat exceeds one or more signal strength thresholds.
 13. The method ofclaim 9, wherein the changing the initial value of the at least oneradio network parameter includes: changing the antenna tilt by a firstangle for the first cell and by a second angle for the plurality ofcells, changing the common pilot channel (CPICH) power by decreasingpilot power usage for the first cell and by increasing the pilot powerusage for the plurality of cells, and changing the handover hysteresisoffset by decreasing the handover hysteresis offset for the first celland by increasing the handover hysteresis offset for the plurality ofcells.
 14. The method of claim 9, wherein the changing the initial valueof the at least one radio network parameter includes decreasing the atleast one radio network parameter of the first cell and increasing theat least one radio network parameter of the plurality of cells tooptimize performance of the first cell.
 15. An apparatus comprising: amemory configured to store instructions; and one or more processorsconfigured to execute the instructions that cause the processor to:change an initial value of at least one radio network parameter for areference cluster of cells, wherein the reference cluster of cellsincludes a first cell and a plurality of cells that neighbor the firstcell and wherein the plurality of cells comprise at least one of intracluster, inter cluster, or Inter Radio Access Technologies (iRAT)cluster, monitor resource loading and at least one performance indicatorof the reference cluster of cells, and repeat changing of the initialvalue and monitoring resource loading until one of: resource loading ofthe reference cluster of cells is decreased below a resource loadingthreshold, or the at least one performance indicator indicates degradedperformance for the reference cluster of cells.
 16. The apparatus ofclaim 15, wherein the at least one radio network parameter is offloadingof communication traffic from the first cell to at least one cell fromamong the plurality of cells that neighbor the first cell.
 17. Theapparatus of claim 15, wherein the one or more processors that areconfigured to execute the instructions that further cause the one ormore processors to: select the first cell for the reference cluster ofcells in a cellular network, the first cell is characterized by having aradio resource utilization that exceeds a predetermined threshold. 18.The apparatus of claim 15, wherein the one or more processors that areconfigured to execute the instructions that further cause the one ormore processors to: select each of the plurality of cells that neighborthe first cell for the reference cluster of cells based on at least oneof: each of the plurality of cells being defined as intra frequencyneighbor cells of the first cell, forming the intra cluster, or each ofthe plurality of cells having a signal strength as measured by userequipment associated with the first cell that exceeds one or more signalstrength thresholds.
 19. The apparatus of claim 18, wherein the one ormore processors that are configured to execute the instructions thatfurther cause the one or more processors to: rank each of the pluralityof cells according to the signal strength to determine different degreesof the plurality of cells that neighbor the first cell.
 20. Theapparatus of claim 15, wherein the one or more processors that areconfigured to execute the instructions that further cause the one ormore processors to: based on a failure to obtain from monitoring theresource loading the at least one performance indicator of the referencecluster of cells for a predetermined time period, setting the at leastone radio network parameter to the initial value, wherein the at leastone radio network parameter includes hardware and software radioparameters.